text: from __future__ import generator_stop
  import collections
  import logging
  import numpy as np
  import platform
  import random
  from typing import List, Dict
  import collections
  import platform
  
  # Import ray before psutil will make sure we use psutil's bundled version
  import ray  # noqa F401
  import psutil  # noqa E402
  
  from ray.rllib.execution.segment_tree import SumSegmentTree, MinSegmentTree
  from ray.rllib.policy.sample_batch import SampleBatch, MultiAgentBatch, \
      DEFAULT_POLICY_ID
  from ray.rllib.utils.annotations import DeveloperAPI
  from ray.util.iter import ParallelIteratorWorker
  from ray.util.debug import log_once
  from ray.rllib.utils.timer import TimerStat
  from ray.rllib.utils.window_stat import WindowStat
  from ray.rllib.utils.typing import SampleBatchType
  
  # Constant that represents all policies in lockstep replay mode.
  _ALL_POLICIES = "__all__"
  
  logger = logging.getLogger(__name__)
  
  
  def warn_replay_buffer_size(*, item: SampleBatchType, num_items: int) -> None:
      """Warn if the configured replay buffer size is too large."""
      if log_once("replay_buffer_size"):
          item_size = item.size_bytes()
          psutil_mem = psutil.virtual_memory()
          total_gb = psutil_mem.total / 1e9
          mem_size = num_items * item_size / 1e9
          msg = ("Estimated max memory usage for replay buffer is {} GB "
                 "({} batches of size {}, {} bytes each), "
                 "available system memory is {} GB".format(
                     mem_size, num_items, item.count, item_size, total_gb))
          if mem_size > total_gb:
              raise ValueError(msg)
          elif mem_size > 0.2 * total_gb:
              logger.warning(msg)
          else:
              logger.info(msg)
  
  
  @DeveloperAPI
  class ReplayBuffer:
      @DeveloperAPI
      def __init__(self, size: int):
          """Create Prioritized Replay buffer.
  
          Args:
              size (int): Max number of timesteps to store in the FIFO buffer.
          """
          self._storage = []
          self._maxsize = size
          self._next_idx = 0
          self._hit_count = np.zeros(size)
          self._eviction_started = False
          self._num_timesteps_added = 0
          self._num_timesteps_added_wrap = 0
          self._num_timesteps_sampled = 0
          self._evicted_hit_stats = WindowStat("evicted_hit", 1000)
          self._est_size_bytes = 0
  
      def __len__(self) -> int:
          return len(self._storage)
  
      @DeveloperAPI
      def add(self, item: SampleBatchType, weight: float) -> None:
          warn_replay_buffer_size(
              item=item, num_items=self._maxsize / item.count)
          assert item.count > 0, item
          self._num_timesteps_added += item.count
          self._num_timesteps_added_wrap += item.count
  
          if self._next_idx >= len(self._storage):
              self._storage.append(item)
              self._est_size_bytes += item.size_bytes()
          else:
              self._storage[self._next_idx] = item
  
          # Wrap around storage as a circular buffer once we hit maxsize.
          if self._num_timesteps_added_wrap >= self._maxsize:
              self._eviction_started = True
              self._num_timesteps_added_wrap = 0
              self._next_idx = 0
          else:
              self._next_idx += 1
  
          if self._eviction_started:
              self._evicted_hit_stats.push(self._hit_count[self._next_idx])
              self._hit_count[self._next_idx] = 0
  
      def _encode_sample(self, idxes: List[int]) -> SampleBatchType:
          out = SampleBatch.concat_samples([self._storage[i] for i in idxes])
          out.decompress_if_needed()
          return out
  
      @DeveloperAPI
      def sample(self, num_items: int) -> SampleBatchType:
          """Sample a batch of experiences.
  
          Args:
              num_items (int): Number of items to sample from this buffer.
  
          Returns:
              SampleBatchType: concatenated batch of items.
          """
          idxes = [
              random.randint(0,
                             len(self._storage) - 1) for _ in range(num_items)
          ]
          self._num_sampled += num_items
          return self._encode_sample(idxes)
  
      @DeveloperAPI
      def stats(self, debug=False) -> dict:
          data = {
              "added_count": self._num_timesteps_added,
              "sampled_count": self._num_timesteps_sampled,
              "est_size_bytes": self._est_size_bytes,
              "num_entries": len(self._storage),
          }
          if debug:
              data.update(self._evicted_hit_stats.stats())
          return data
  
  
  @DeveloperAPI
  class PrioritizedReplayBuffer(ReplayBuffer):
      @DeveloperAPI
      def __init__(self, size: int, alpha: float):
          """Create Prioritized Replay buffer.
  
          Args:
              size (int): Max number of items to store in the FIFO buffer.
              alpha (float): how much prioritization is used
                  (0 - no prioritization, 1 - full prioritization).
  
          See also:
              ReplayBuffer.__init__()
          """
          super(PrioritizedReplayBuffer, self).__init__(size)
          assert alpha > 0
          self._alpha = alpha
  
          it_capacity = 1
          while it_capacity < size:
              it_capacity *= 2
  
          self._it_sum = SumSegmentTree(it_capacity)
          self._it_min = MinSegmentTree(it_capacity)
          self._max_priority = 1.0
          self._prio_change_stats = WindowStat("reprio", 1000)
  
      @DeveloperAPI
      def add(self, item: SampleBatchType, weight: float) -> None:
          idx = self._next_idx
          super(PrioritizedReplayBuffer, self).add(item, weight)
          if weight is None:
              weight = self._max_priority
          self._it_sum[idx] = weight**self._alpha
          self._it_min[idx] = weight**self._alpha
  
      def _sample_proportional(self, num_items: int) -> List[int]:
          res = []
          for _ in range(num_items):
              # TODO(szymon): should we ensure no repeats?
              mass = random.random() * self._it_sum.sum(0, len(self._storage))
              idx = self._it_sum.find_prefixsum_idx(mass)
              res.append(idx)
          return res
  
      @DeveloperAPI
      def sample(self, num_items: int, beta: float) -> SampleBatchType:
          """Sample a batch of experiences and return priority weights, indices.
  
          Args:
              num_items (int): Number of items to sample from this buffer.
              beta (float): To what degree to use importance weights
                  (0 - no corrections, 1 - full correction).
  
          Returns:
              SampleBatchType: Concatenated batch of items including "weights"
                  and "batch_indexes" fields denoting IS of each sampled
                  transition and original idxes in buffer of sampled experiences.
          """
          assert beta >= 0.0
  
          idxes = self._sample_proportional(num_items)
  
          weights = []
          batch_indexes = []
          p_min = self._it_min.min() / self._it_sum.sum()
          max_weight = (p_min * len(self._storage))**(-beta)
  
          for idx in idxes:
              p_sample = self._it_sum[idx] / self._it_sum.sum()
              weight = (p_sample * len(self._storage))**(-beta)
              count = self._storage[idx].count
              weights.extend([weight / max_weight] * count)
              batch_indexes.extend([idx] * count)
              self._num_timesteps_sampled += count
          batch = self._encode_sample(idxes)
  
          # Note: prioritization is not supported in lockstep replay mode.
          if isinstance(batch, SampleBatch):
              assert len(weights) == batch.count
              assert len(batch_indexes) == batch.count
              batch["weights"] = np.array(weights)
              batch["batch_indexes"] = np.array(batch_indexes)
  
          return batch
  
      @DeveloperAPI
      def update_priorities(self, idxes: List[int],
                            priorities: List[float]) -> None:
          """Update priorities of sampled transitions.
  
          sets priority of transition at index idxes[i] in buffer
          to priorities[i].
  
          Parameters
          ----------
          idxes: [int]
            List of idxes of sampled transitions
          priorities: [float]
            List of updated priorities corresponding to
            transitions at the sampled idxes denoted by
            variable `idxes`.
          """
          assert len(idxes) == len(priorities)
          for idx, priority in zip(idxes, priorities):
              assert priority > 0
              assert 0 <= idx < len(self._storage)
              delta = priority**self._alpha - self._it_sum[idx]
              self._prio_change_stats.push(delta)
              self._it_sum[idx] = priority**self._alpha
              self._it_min[idx] = priority**self._alpha
  
              self._max_priority = max(self._max_priority, priority)
  
      @DeveloperAPI
      def stats(self, debug: bool = False) -> Dict:
          parent = ReplayBuffer.stats(self, debug)
          if debug:
              parent.update(self._prio_change_stats.stats())
          return parent
  
  
  # Visible for testing.
  _local_replay_buffer = None
  
  
  class LocalReplayBuffer(ParallelIteratorWorker):
      """A replay buffer shard.
  
      Ray actors are single-threaded, so for scalability multiple replay actors
      may be created to increase parallelism."""
  
      def __init__(self,
                   num_shards: int = 1,
                   learning_starts: int = 1000,
                   buffer_size: int = 10000,
                   replay_batch_size: int = 1,
                   prioritized_replay_alpha: float = 0.6,
                   prioritized_replay_beta: float = 0.4,
                   prioritized_replay_eps: float = 1e-6,
                   replay_mode: str = "independent",
                   replay_sequence_length: int = 1):
          self.replay_starts = learning_starts // num_shards
          self.buffer_size = buffer_size // num_shards
          self.replay_batch_size = replay_batch_size
          self.prioritized_replay_beta = prioritized_replay_beta
          self.prioritized_replay_eps = prioritized_replay_eps
          self.replay_mode = replay_mode
          self.replay_sequence_length = replay_sequence_length
  
          if replay_sequence_length > 1:
              self.replay_batch_size = int(
                  max(1, replay_batch_size // replay_sequence_length))
              logger.info(
                  "Since replay_sequence_length={} and replay_batch_size={}, "
                  "we will replay {} sequences at a time.".format(
                      replay_sequence_length, replay_batch_size,
                      self.replay_batch_size))
  
          if replay_mode not in ["lockstep", "independent"]:
              raise ValueError("Unsupported replay mode: {}".format(replay_mode))
  
          def gen_replay():
              while True:
                  yield self.replay()
  
          ParallelIteratorWorker.__init__(self, gen_replay, False)
  
          def new_buffer():
              return PrioritizedReplayBuffer(
                  self.buffer_size, alpha=prioritized_replay_alpha)
  
          self.replay_buffers = collections.defaultdict(new_buffer)
  
          # Metrics
          self.add_batch_timer = TimerStat()
          self.replay_timer = TimerStat()
          self.update_priorities_timer = TimerStat()
          self.num_added = 0
  
          # Make externally accessible for testing.
          global _local_replay_buffer
          _local_replay_buffer = self
          # If set, return this instead of the usual data for testing.
          self._fake_batch = None
  
      @staticmethod
      def get_instance_for_testing():
          global _local_replay_buffer
          return _local_replay_buffer
  
      def get_host(self) -> str:
          return platform.node()
  
      def add_batch(self, batch: SampleBatchType) -> None:
          # Make a copy so the replay buffer doesn't pin plasma memory.
          batch = batch.copy()
          # Handle everything as if multiagent
          if isinstance(batch, SampleBatch):
              batch = MultiAgentBatch({DEFAULT_POLICY_ID: batch}, batch.count)
          with self.add_batch_timer:
              if self.replay_mode == "lockstep":
                  # Note that prioritization is not supported in this mode.
                  for s in batch.timeslices(self.replay_sequence_length):
                      self.replay_buffers[_ALL_POLICIES].add(s, weight=None)
              else:
                  for policy_id, b in batch.policy_batches.items():
                      for s in b.timeslices(self.replay_sequence_length):
                          if "weights" in s:
                              weight = np.mean(s["weights"])
                          else:
                              weight = None
                          self.replay_buffers[policy_id].add(s, weight=weight)
          self.num_added += batch.count
  
      def replay(self) -> SampleBatchType:
          if self._fake_batch:
              fake_batch = SampleBatch(self._fake_batch)
              return MultiAgentBatch({
                  DEFAULT_POLICY_ID: fake_batch
              }, fake_batch.count)
  
          if self.num_added < self.replay_starts:
              return None
  
          with self.replay_timer:
              if self.replay_mode == "lockstep":
                  return self.replay_buffers[_ALL_POLICIES].sample(
                      self.replay_batch_size, beta=self.prioritized_replay_beta)
              else:
                  samples = {}
                  for policy_id, replay_buffer in self.replay_buffers.items():
                      samples[policy_id] = replay_buffer.sample(
                          self.replay_batch_size,
                          beta=self.prioritized_replay_beta)
                  return MultiAgentBatch(samples, self.replay_batch_size)
  
      def update_priorities(self, prio_dict: Dict) -> None:
          with self.update_priorities_timer:
              for policy_id, (batch_indexes, td_errors) in prio_dict.items():
                  new_priorities = (
                      np.abs(td_errors) + self.prioritized_replay_eps)
                  self.replay_buffers[policy_id].update_priorities(
                      batch_indexes, new_priorities)
  
      def stats(self, debug: bool = False) -> Dict:
          stat = {
              "add_batch_time_ms": round(1000 * self.add_batch_timer.mean, 3),
              "replay_time_ms": round(1000 * self.replay_timer.mean, 3),
              "update_priorities_time_ms": round(
                  1000 * self.update_priorities_timer.mean, 3),
          }
          for policy_id, replay_buffer in self.replay_buffers.items():
              stat.update({
                  "policy_{}".format(policy_id): replay_buffer.stats(debug=debug)
              })
          return stat
  
  
  # Visible for testing.
  _local_vanilla_replay_buffer = None
  
  
  class LocalVanillaReplayBuffer(LocalReplayBuffer):
      """A replay buffer shard.
  
      Ray actors are single-threaded, so for scalability multiple replay actors
      may be created to increase parallelism."""
  
      def __init__(
          self,
          num_shards,
          learning_starts,
          buffer_size,
          replay_batch_size,
          prioritized_replay_alpha=0.6,
          prioritized_replay_beta=0.4,
          prioritized_replay_eps=1e-6,
          replay_mode: str = "independent",
          multiagent_sync_replay=False,
      ):
          self.replay_starts = learning_starts // num_shards
          self.buffer_size = buffer_size // num_shards
          self.replay_batch_size = replay_batch_size
          self.prioritized_replay_beta = prioritized_replay_beta
          self.prioritized_replay_eps = prioritized_replay_eps
          self.replay_mode = replay_mode
          self.multiagent_sync_replay = multiagent_sync_replay
  
          if replay_mode not in ["lockstep", "independent"]:
              raise ValueError("Unsupported replay mode: {}".format(replay_mode))
  
          def gen_replay():
              while True:
                  yield self.replay()
  
          ParallelIteratorWorker.__init__(self, gen_replay, False)
  
          def new_buffer():
              return ReplayBuffer(self.buffer_size)
  
          self.replay_buffers = collections.defaultdict(new_buffer)
  
          # Metrics
          self.add_batch_timer = TimerStat()
          self.replay_timer = TimerStat()
          self.update_priorities_timer = TimerStat()
          self.num_added = 0
  
          # Make externally accessible for testing.
          global _local_vanilla_replay_buffer
          _local_vanilla_replay_buffer = self
          # If set, return this instead of the usual data for testing.
          self._fake_batch = None
  
      @staticmethod
      def get_instance_for_testing():
          global _local_vanilla_replay_buffer
          return _local_vanilla_replay_buffer
  
      def replay(self):
          if self._fake_batch:
              fake_batch = SampleBatch(self._fake_batch)
              return MultiAgentBatch({DEFAULT_POLICY_ID: fake_batch}, fake_batch.count)
  
          if self.num_added < self.replay_starts:
              return None
  
          with self.replay_timer:
              if self.replay_mode == "lockstep":
                  return self.replay_buffers[_ALL_POLICIES].sample(
                      self.replay_batch_size)
              else:
                  samples = {}
                  for policy_id, replay_buffer in self.replay_buffers.items():
                      samples[policy_id] = replay_buffer.sample(
                          self.replay_batch_size)
                  return MultiAgentBatch(samples, self.replay_batch_size)
  
      def stats(self, debug=False):
          stat = {
              "add_batch_time_ms": round(1000 * self.add_batch_timer.mean, 3),
              "replay_time_ms": round(1000 * self.replay_timer.mean, 3),
          }
          for policy_id, replay_buffer in self.replay_buffers.items():
              stat.update(
                  {"policy_{}".format(policy_id): replay_buffer.stats(debug=debug)}
              )
          return stat
  
  
  VanillaReplayActor = ray.remote(num_cpus=0)(LocalVanillaReplayBuffer)
  
  ReplayActor = ray.remote(num_cpus=0)(LocalReplayBuffer)
  